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Brain decoding (i.e., retrieving information from brain signals by employing machine learning algorithms) has recently received considerable attention across many communities. In a typical brain decoding paradigm, different types of stimuli are shown to the participant of the neuroimaging experiment, while his/her concurrent brain activity is captured using neuroimaging techniques. Then a machine...
Decoding human emotion induced by visual stimuli from brain signal retrieved by functional magnetic resonance imaging are proposed. Brain decoding technique with support vector machine is used to predict human emotion. Human subjective experiment are conducted by five subjects and the result shows that the accuracy of prediction is around 70% for two subjects and around 50% for other two subjects...
Machine learning from brain images is a central tool for image-based diagnosis and diseases characterization. Predicting behavior from functional imaging, brain decoding, analyzes brain activity in terms of the behavior that it implies. While these multivariate techniques are becoming standard brain mapping tools, like mass-univariate analysis, they entail much larger computational costs. In an time...
We study subjects with pharmacologically intractable epilepsy who undergo semi-chronic implantation of electrodes for clinical purposes. We record physiological activity from tens to more than one hundred electrodes implanted in different parts of neocortex. These recordings provide higher spatial and temporal resolution than non-invasive measures of human brain activity. Here we discuss our efforts...
Decoding perceptual or cognitive states based on brain activity measured using functional Magnetic Resonance Imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low Signal-to-Noise Ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple...
On the basis of synchronous oscillation in the visual cortex and synchronized responses to external stimuli, we have proposed a complete neural computational model of visual information processing, which consists of multiscale filtering, phase synchronization, and inner-product formation. In the model, firing-spike trains are topologically mapped from the retina to the cortex V1 and are synchronously...
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